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Clusters of interannual sea ice variability in the northern hemisphere

Abstract

We determine robust modes of the northern hemisphere (NH) sea ice variability on interannual timescales disentangled from the long-term climate change. This study focuses on sea ice thickness (SIT), reconstructed with an ocean–sea-ice general circulation model, because SIT has a potential to contain most of the interannual memory and predictability of the NH sea ice system. We use the K-means cluster analysis—one of clustering methods that partition data into groups or clusters based on their distances in the physical space without the typical constraints of other unsupervised learning statistical methods such as the widely-used principal component analysis. To adequately filter out climate change signal in the Arctic from 1958 to 2013 we have to approximate it with a 2nd degree polynomial. Using 2nd degree residuals of SIT leads to robust K-means cluster patterns, i.e. invariant to further increase of the polynomial degree. A set of clustering validity indices yields K = 3 as the optimal number of SIT clusters for all considered months and seasons with strong similarities in their cluster patterns. The associated time series of cluster occurrences exhibit predominant interannual persistence with mean timescale of about 2 years. Compositing analysis of the NH surface climate conditions associated with each cluster indicates that wind forcing seem to be the key factor driving the formation of interannual SIT cluster patterns during the winter. Climate memory in SIT with such interannual persistence could lead to increased predictability of the Artic sea ice cover beyond seasonal timescales.

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Acknowledgments

The authors acknowledge funding support for this study from the PICA-ICE (CGL2012-31987) project funded by the Ministry of Economy and Competitiveness of Spain and the SPECS (ENV-2012-308378) project funded by the Seventh Framework Programme (FP7) of the European Commission. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center in Barcelona, Spain, and by the European Centre for Medium–Range Weather Forecasts in Reading, UK. The authors thank the reviewers for their constructive input, and Matthieu Chevallier, Ed Hawkins, Jonathan J. Day, Steffen Tietsche, Edward Blanchard-Wrigglesworth and Javier Garcia-Serrano for valuable discussions. Analyzed sea ice cover reconstructions with ORCA1 NEMO-LIM2 are available upon request. The new developed R functions, by Neven S. Fučkar and Virginie Guemas, used in this study are publicly available in the s2dverification package from the CRAN (http://cran.r-project.org). The s2dverification package has been continuously developed at the Climate Forecasting Unit of the Catalan Institute of Climate Sciences (IC3) and now at the Earth Sciences Department at the Barcelona Supercomputing Center (BSC-ES).

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Correspondence to Neven S. Fučkar.

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Fučkar, N.S., Guemas, V., Johnson, N.C. et al. Clusters of interannual sea ice variability in the northern hemisphere. Clim Dyn 47, 1527–1543 (2016). https://doi.org/10.1007/s00382-015-2917-2

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Keywords

  • Arctic sea ice
  • GCM reconstruction
  • K-means cluster analysis
  • Climate change
  • Interannual variability